Jose Andres Millan-Romera — accepted AI-conference papers

2025

Tightly Coupled SLAM With Imprecise Architectural Plans

Muhammad Shaheer, Jose Andres Millan-Romera, Hriday Bavle, Marco Giberna, Jose Luis Sanchez-Lopez, Javier Civera +1

RA-L 2025

Robots navigating indoor environments often have access to architectural plans, which can serve as prior knowledge to enhance their localization and mapping capabilities. While some SLAM algorithms leverage these plans for global localization in real-world environments, they typically overlook a cri

Cited by 4 Source ↗Scholar ↗

2024

Learning High-level Semantic-Relational Concepts for SLAM

Jose Andres Millan-Romera, Hriday Bavle, Muhammad Shaheer, Martin R. Oswald, Holger Voos, Jose Luis Sanchez-Lopez

IROS 2024poster

Recent works on SLAM extend their pose graphs with higher-level semantic concepts like Rooms exploiting relationships between them, to provide, not only a richer representation of the situation/environment but also to improve the accuracy of its estimation. Concretely, our previous work, Situational…

Cited by 1 Source ↗Scholar ↗

2023

Graph-Based Global Robot Localization Informing Situational Graphs with Architectural Graphs

Muhammad Shaheer, Jose Andres Millan-Romera, Hriday Bavle, Jose Luis Sanchez-Lopez, Javier Civera, Holger Voos

IROS 2023poster

In this paper, we propose a solution for legged robot localization using architectural plans. Our specific contributions towards this goal are several. Firstly, we develop a method for converting the plan of a building into what we denote as an architectural graph (A-Graph). When the robot starts mo…

Cited by 11 Source ↗Scholar ↗